TFProtBert:使用ProtBert潜伏空间表示方法检测与甲基化DNA结合的转录因子
Saima Gaffar1, Kil To Chong1,2, Hilal Tayara3
1Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea.
International journal of molecular sciences
|May 14, 2025
概括
本研究引入了一个计算框架,以识别转录因子 (TF) 和转录因子偏好甲基化DNA (TFPMs). 使用ProtBert和SVM的新型两层模型准确地检测到这些关键的基因表达调节器.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 分子生物学分子生物学
背景情况:
- 转录因子 (TF) 对于调节基因表达和细胞过程至关重要.
- 识别TF对于理解基因表达机制至关重要.
- 用于TF识别的实验方法是耗时和劳动密集型的,需要计算方法.
研究的目的:
- 开发一个有效的计算模型来识别转录因子 (TF).
- 具体确定那些更喜欢与甲基化DNA (TFPMs) 结合的TFs.
- 通过计算预测来克服实验方法的局限性.
主要方法:
- 开发了一个双层预测框架.
- 该框架使用了一个支持向量机器 (SVM),与ProtBert的潜在空间表示集成.
- 该模型在一个不平衡的数据库上进行了评估,并与现有方法进行了比较.
主要成果:
- 第一个层成功地预测并识别了转录因子 (TF).
- 第二层准确地预测并确定了偏好甲基化DNA (TFPMs) 的转录因子.
- 与最先进的方法相比,拟议的模型在检测TF和TFPM方面表现优越.
结论:
- 开发的计算框架提供了一种可靠和有效的方法来识别TF和TFPMs.
- 这种方法比传统的实验技术有了显著的进步.
- 该模型的有效性,即使在不平衡的数据集上,也突出了其在基因表达研究中的稳定性.
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